Cooperative optimization of cutting parameters, process routes, and scheduling considering carbon emissions with analytic target cascading

2021 ◽  
Author(s):  
Changle Tian ◽  
Guanghui Zhou ◽  
Fengyi Lu ◽  
Zhenghao Chen ◽  
Liang Zou
Keyword(s):  
Carbon Emissions ◽  
Cutting Parameters ◽  
Target Cascading ◽  
Optimization Of Cutting Parameters

scholarly journals Optimization of Cutting Parameters for Trade-off Among Carbon Emissions, Surface Roughness, and Processing Time

2019 ◽  
Vol 32 (1) ◽  
Author(s):  
Zhipeng Jiang ◽  
Dong Gao ◽  
Yong Lu ◽  
Xianli Liu
Keyword(s):  
Surface Roughness ◽  
Carbon Emissions ◽  
Processing Time ◽  
Cutting Parameters ◽  
Machining Process ◽  
Trade Off ◽  
Pareto Fronts ◽  
Optimization Of Cutting Parameters ◽  
1045 Steel ◽  
Optimal Cutting Parameters

AbstractAs the manufacturing industry is facing increasingly serious environmental problems, because of which carbon tax policies are being implemented, choosing the optimum cutting parameters during the machining process is crucial for automobile panel dies in order to achieve synergistic minimization of the environment impact, product quality, and processing efficiency. This paper presents a processing task-based evaluation method to optimize the cutting parameters, considering the trade-off among carbon emissions, surface roughness, and processing time. Three objective models and their relationships with the cutting parameters were obtained through input–output, response surface, and theoretical analyses, respectively. Examples of cylindrical turning were applied to achieve a central composite design (CCD), and relative validation experiments were applied to evaluate the proposed method. The experiments were conducted on the CAK50135di lathe cutting of AISI 1045 steel, and NSGA-II was used to obtain the Pareto fronts of the three objectives. Based on the TOPSIS method, the Pareto solution set was ranked to find the optimal solution to evaluate and select the optimal cutting parameters. An S/N ratio analysis and contour plots were applied to analyze the influence of each decision variable on the optimization objective. Finally, the changing rules of a single factor for each objective were analyzed. The results demonstrate that the proposed method is effective in finding the trade-off among the three objectives and obtaining reasonable application ranges of the cutting parameters from Pareto fronts.

Optimization of Machining Process Planning for Carbon Reduction

2018 ◽  
Author(s):  
Zhaorui Dong ◽  
Qiong Liu ◽  
Qin Li
Keyword(s):  
Process Planning ◽  
Discrete Optimization ◽  
Carbon Emissions ◽  
Continuous Optimization ◽  
Cutting Parameters ◽  
Machining Process ◽  
Nsga Ii ◽  
Carbon Reduction ◽  
Proposed Model ◽  
Optimization Of Cutting Parameters

Most researches on process planning optimized machining process routings and cutting parameters independently and ignored their comprehensive effects on carbon reduction. In order to further reduce carbon emissions in manufacturing processes, an optimization model of cutting parameters and machining process routings is proposed to minimize total carbon emissions and total processing time of all processes. Carbon emissions include those caused by energy consumptions of machines in cutting state, material consumption of cutting tools and cutting fluid in all processes. As the optimization of cutting parameters is a continuous optimization problem, but the optimization of machining process routings including machining methods, process sequences, machine allocating and cutter selecting are discrete optimization problems, the whole optimization of process planning is divided into two parts. One is continuous optimization of cutting parameters. Another is discrete optimization of machining process routings. A hybrid optimization strategy of bird swarm algorithm (BSA) and NSGA-II algorithm is proposed to optimize the proposed model. Cutting parameters are optimized using BSA aiming at minimizing carbon emissions and machining time of each process. Machining process routings are optimized using NSGA-II under each optimized group of cutting parameters from the Pareto set. Four kinds of mutation operators in NSGA-II are designed for the discrete optimization of machining process routings. A workpiece with six machining features to be machined in a workshop with two CNC lathes, two CNC milling machines and two drilling machines is taken as a case study. The validity of the proposed model and hybrid strategy is verified by computational and analytical results. Several conclusions are yielded.

Optimization of Cutting Parameters Considering Surface Roughness When Milling AISI 304 Steel Using Coated Wc-Co and Tialn / Wc-Co Coated

2019 ◽  
Author(s):  
Murilo Pereira Lopes ◽  
Jose Rubens Gonçalves Carneiro ◽  
Gilmar Cordeiro da Silva ◽  
Carlos Eduardo Santos ◽  
Ítalo Bruno dos Santos
Keyword(s):  
Surface Roughness ◽  
Cutting Parameters ◽  
Aisi 304 ◽  
Aisi 304 Steel ◽  
Optimization Of Cutting Parameters

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

Optimization of Cutting Parameters in Milling Process Using Genetic Algorithm and ANOVA (March 2020)

2020 ◽  
Vol 38 (10A) ◽  
pp. 1489-1503
Author(s):  
Marwa Q. Ibraheem
Keyword(s):  
Genetic Algorithm ◽  
Tool Wear ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Optimal Value ◽  
Optimization Of Cutting Parameters

In this present work use a genetic algorithm for the selection of cutting conditions in milling operation such as cutting speed, feed and depth of cut to investigate the optimal value and the effects of it on the material removal rate and tool wear. The material selected for this work was Ti-6Al-4V Alloy using H13A carbide as a cutting tool. Two objective functions have been adopted gives minimum tool wear and maximum material removal rate that is simultaneously optimized. Finally, it does conclude from the results that the optimal value of cutting speed is (1992.601m/min), depth of cut is (1.55mm) and feed is (148.203mm/rev) for the present work.

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